Update app.py

app.py

@@ -22,13 +22,6 @@ if not os.path.exists(extract_dir):
 
 model_tf = tf.saved_model.load(extract_dir)
 
-# --- Inspección de firma del modelo TensorFlow ---
-print("\n\n🔍 FIRMA DEL MODELO TENSORFLOW:")
-for key, func in model_tf.signatures.items():
-    print(f"Firma: {key}")
-    print("Entradas:", func.structured_input_signature)
-    print("Salidas:", func.structured_outputs)
-
 # Función helper para inferencia TensorFlow
 def predict_tf(img: Image.Image):
     try:
@@ -46,7 +39,7 @@ def predict_tf(img: Image.Image):
         return probs
     except Exception as e:
         print(f"Error en predict_tf: {e}")
-        return np.zeros(len(CLASSES))
+        return np.zeros(2)
 
 MODEL_NAME = "ahishamm/vit-base-HAM-10000-sharpened-patch-32"
 feature_extractor = ViTImageProcessor.from_pretrained(MODEL_NAME)
@@ -81,8 +74,7 @@ def analizar_lesion_combined(img):
         pred_idx_vit = int(np.argmax(probs_vit))
         pred_class_vit = CLASSES[pred_idx_vit]
         confidence_vit = probs_vit[pred_idx_vit]
-    except Exception as e:
-        print(f"Error en ViT prediction: {e}")
+    except:
         pred_class_vit = "Error"
         confidence_vit = 0.0
         probs_vit = np.zeros(len(CLASSES))
@@ -90,26 +82,24 @@ def analizar_lesion_combined(img):
     try:
         pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
         prob_malignant = float(probs_fast_mal[1])
-    except Exception as e:
-        print(f"Error en Fast.ai malignancy: {e}")
+    except:
         prob_malignant = 0.0
 
     try:
-        pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai)
-    except Exception as e:
-        print(f"Error en Fast.ai tipo: {e}")
+        pred_fast_type, _, _ = model_norm2000.predict(img_fastai)
+    except:
         pred_fast_type = "Error"
 
     try:
         probs_tf = predict_tf(img)
-        pred_idx_tf = int(np.argmax(probs_tf))
-        confidence_tf = probs_tf[pred_idx_tf]
-        if pred_idx_tf < len(CLASSES):
-            pred_class_tf = CLASSES[pred_idx_tf]
+        if len(probs_tf) == 2:
+            benign_prob, malignant_prob = probs_tf
+            pred_class_tf = "Maligno" if malignant_prob > benign_prob else "Benigno"
+            confidence_tf = max(probs_tf)
         else:
-            pred_class_tf = f"Clase desconocida (\u00edndice {pred_idx_tf})"
-    except Exception as e:
-        print(f"Error en TensorFlow prediction: {e}")
+            pred_class_tf = "Modelo no binario"
+            confidence_tf = 0.0
+    except:
         pred_class_tf = "Error"
         confidence_tf = 0.0
 
@@ -131,7 +121,7 @@ def analizar_lesion_combined(img):
 
     informe = f"""
     <div style="font-family:sans-serif; max-width:800px; margin:auto">
-    <h2>🧪 Diagnóstico por 4 modelos de IA</h2>
+    <h2>🦢 Diagnóstico por 4 modelos de IA</h2>
     <table style="border-collapse: collapse; width:100%; font-size:16px">
         <tr><th style="text-align:left">🔍 Modelo</th><th>Resultado</th><th>Confianza</th></tr>
         <tr><td>🧠 ViT (transformer)</td><td><b>{pred_class_vit}</b></td><td>{confidence_vit:.1%}</td></tr>
@@ -140,7 +130,7 @@ def analizar_lesion_combined(img):
         <tr><td>🔬 TensorFlow (saved_model)</td><td><b>{pred_class_tf}</b></td><td>{confidence_tf:.1%}</td></tr>
     </table>
     <br>
-    <b>🧪 Recomendación automática:</b><br>
+    <b>🦥 Recomendación automática:</b><br>
     """
 
     cancer_risk_score = sum(probs_vit[i] * RISK_LEVELS[i]['weight'] for i in range(7))
@@ -154,10 +144,8 @@ def analizar_lesion_combined(img):
         informe += "✅ <b>BAJO RIESGO</b> – Seguimiento de rutina (3-6 meses)"
 
     informe += "</div>"
-
     return informe, html_chart
 
-# Interfaz Gradio
 demo = gr.Interface(
     fn=analizar_lesion_combined,
     inputs=gr.Image(type="pil", label="Sube una imagen de la lesión"),
@@ -170,5 +158,3 @@ demo = gr.Interface(
 if __name__ == "__main__":
     demo.launch()
 
-
-